import os
import pandas as pd
import numpy as np
import argparse
import datasets
import torch

from typing import List
from tqdm import tqdm
from transformers.trainer_utils import set_seed


'''
wget https://huggingface.co/datasets/ceval/ceval-exam/resolve/main/ceval-exam.zip
mkdir data/ceval
mv ceval-exam.zip data/ceval
cd data/ceval; unzip ceval-exam.zip
cd ../../
python evaluate_ceval.py -d data/ceval/
'''

def load_models_tokenizer(args):
    from transformers import AutoModelForCausalLM, AutoTokenizer
    from transformers.generation import GenerationConfig

    tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True).eval()
    model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
    return model, tokenizer


def format_example(line, include_answer=True):
    example = '问题：' + line['question']
    for choice in choices:
        example += f'\n{choice}. {line[f"{choice}"]}'
   
    if include_answer:
        example += '\n答案：' + line["answer"] + '\n\n'
    else:
        example += '\n答案：'
    return example


def generate_few_shot_prompt(k, subject, dev_df):
    prompt = ''
    if k == -1:
        k = dev_df.shape[0]
    for i in range(k):
        prompt += format_example(
            dev_df.iloc[i, :],
            include_answer=True,
        )
    return prompt


def get_logits(tokenizer, model, inputs: List[str]):
    input_ids = tokenizer(inputs, padding=False)['input_ids']
    input_ids = torch.tensor(input_ids, device=model.device)
    tokens = {'input_ids': input_ids}

    outputs = model(input_ids)['logits']
    logits = outputs[:, -1, :]
    log_probs = torch.nn.functional.softmax(logits, dim=-1)
    return log_probs, {'tokens': tokens}


@torch.no_grad()
def eval_subject(
        model,
        tokenizer,
        subject_name,
        test_df,
        k=5,
        dev_df=None,
        few_shot=False,
        save_result_dir=None,
        **kwargs
):
    result = []
    score = []

    few_shot_prompt = generate_few_shot_prompt(
        k, subject_name, dev_df) if few_shot else []
    all_probs = {'prob_A': [], 'prob_B': [], 'prob_C': [], 'prob_D': []}
    if args.debug: print(f"few_shot_prompt: {few_shot_prompt}")

    for _, row in tqdm(test_df.iterrows(), total=len(test_df)):
        question = format_example(row, include_answer=False)
        full_prompt = few_shot_prompt + question

        output, input_info = get_logits(tokenizer, model, [full_prompt])
        assert output.shape[0] == 1
        logits = output.flatten()

        softval = torch.nn.functional.softmax(
                torch.tensor(
                    [
                        logits[tokenizer("A")['input_ids']],
                        logits[tokenizer("B")['input_ids']],
                        logits[tokenizer("C")['input_ids']],
                        logits[tokenizer("D")['input_ids']],
                    ]
                ),
                dim=0,
            )
        if softval.dtype in {torch.bfloat16, torch.float16}:
            softval = softval.to(dtype=torch.float32)
        probs = softval.detach().cpu().numpy()

        for i, choice in enumerate(choices):
            all_probs[f'prob_{choice}'].append(probs[i])
        pred = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(probs)]
        
        if 'answer' in row:
            correct = 1 if pred == row['answer'] else 0
            score.append(correct)
            if args.debug: print(f'{question} pred: {pred} ref: {row["answer"]}')
        result.append(pred)

    if score:
        correct_ratio = 100 * sum(score) / len(score)
        if args.debug: print(subject_name, correct_ratio)
    else:
        correct_ratio = 0
    if save_result_dir:
        test_df['model_output'] = result
        for i, choice in enumerate(choices):
            test_df[f'prob_{choice}'] = (all_probs[f'prob_{choice}'])
        if score:
            test_df["correctness"] = score
        os.makedirs(save_result_dir, exist_ok=True)
        test_df.to_csv(os.path.join(
            save_result_dir, f'{subject_name}_result.csv'), encoding="utf-8", index=False)

    return correct_ratio


def cal_ceval(res):
    acc_sum_dict = dict()
    acc_norm_sum_dict = dict()
    cnt_dict = dict()
    acc_sum = 0.
    cnt = 0
    hard_cnt = 0
    hard_acc_sum = 0.
    for tt in res.keys():
        name = tt.split('-')[-1]
        acc_sum += float(res[tt])
        cnt += 1
        class_ = TASK_NAME_MAPPING[name][2]
        if class_ not in acc_sum_dict:
            acc_sum_dict[class_] = 0.
            acc_norm_sum_dict[class_] = 0.
            cnt_dict[class_] = 0.
        if name in hard_list:
            hard_cnt += 1
            hard_acc_sum += float(res[tt])
        acc_sum_dict[class_] += float(res[tt])
        cnt_dict[class_] += 1
    print('\n\n\n')
    for k in ['STEM', 'Social Science', 'Humanities', 'Other']:
        if k in cnt_dict:
            print('%s acc: %.2f ' % (
                k, acc_sum_dict[k] / cnt_dict[k]))
    if hard_cnt > 0:
        print('Hard acc:%.2f ' % (hard_acc_sum / hard_cnt))
    print('AVERAGE acc:%.2f ' % (acc_sum / cnt))


TASK_NAME_MAPPING = {
    "computer_network": ["Computer Network", "\u8ba1\u7b97\u673a\u7f51\u7edc", "STEM"],
    "operating_system": ["Operating System", "\u64cd\u4f5c\u7cfb\u7edf", "STEM"],
    "computer_architecture": ["Computer Architecture", "\u8ba1\u7b97\u673a\u7ec4\u6210", "STEM"],
    "college_programming": ["College Programming", "\u5927\u5b66\u7f16\u7a0b", "STEM"],
    "college_physics": ["College Physics", "\u5927\u5b66\u7269\u7406", "STEM"],
    "college_chemistry": ["College Chemistry", "\u5927\u5b66\u5316\u5b66", "STEM"],
    "advanced_mathematics": ["Advanced Mathematics", "\u9ad8\u7b49\u6570\u5b66", "STEM"],
    "probability_and_statistics": ["Probability and Statistics", "\u6982\u7387\u7edf\u8ba1", "STEM"],
    "discrete_mathematics": ["Discrete Mathematics", "\u79bb\u6563\u6570\u5b66", "STEM"],
    "electrical_engineer": ["Electrical Engineer", "\u6ce8\u518c\u7535\u6c14\u5de5\u7a0b\u5e08", "STEM"],
    "metrology_engineer": ["Metrology Engineer", "\u6ce8\u518c\u8ba1\u91cf\u5e08", "STEM"],
    "high_school_mathematics": ["High School Mathematics", "\u9ad8\u4e2d\u6570\u5b66", "STEM"],
    "high_school_physics": ["High School Physics", "\u9ad8\u4e2d\u7269\u7406", "STEM"],
    "high_school_chemistry": ["High School Chemistry", "\u9ad8\u4e2d\u5316\u5b66", "STEM"],
    "high_school_biology": ["High School Biology", "\u9ad8\u4e2d\u751f\u7269", "STEM"],
    "middle_school_mathematics": ["Middle School Mathematics", "\u521d\u4e2d\u6570\u5b66", "STEM"],
    "middle_school_biology": ["Middle School Biology", "\u521d\u4e2d\u751f\u7269", "STEM"],
    "middle_school_physics": ["Middle School Physics", "\u521d\u4e2d\u7269\u7406", "STEM"],
    "middle_school_chemistry": ["Middle School Chemistry", "\u521d\u4e2d\u5316\u5b66", "STEM"],
    "veterinary_medicine": ["Veterinary Medicine", "\u517d\u533b\u5b66", "STEM"],
    "college_economics": ["College Economics", "\u5927\u5b66\u7ecf\u6d4e\u5b66", "Social Science"],
    "business_administration": ["Business Administration", "\u5de5\u5546\u7ba1\u7406", "Social Science"],
    "marxism": ["Marxism", "\u9a6c\u514b\u601d\u4e3b\u4e49\u57fa\u672c\u539f\u7406", "Social Science"],
    "mao_zedong_thought": ["Mao Zedong Thought", "\u6bdb\u6cfd\u4e1c\u601d\u60f3\u548c\u4e2d\u56fd\u7279\u8272\u793e\u4f1a\u4e3b\u4e49\u7406\u8bba\u4f53\u7cfb\u6982\u8bba", "Social Science"],
    "education_science": ["Education Science", "\u6559\u80b2\u5b66", "Social Science"],
    "teacher_qualification": ["Teacher Qualification", "\u6559\u5e08\u8d44\u683c", "Social Science"],
    "high_school_politics": ["High School Politics", "\u9ad8\u4e2d\u653f\u6cbb", "Social Science"],
    "high_school_geography": ["High School Geography", "\u9ad8\u4e2d\u5730\u7406", "Social Science"],
    "middle_school_politics": ["Middle School Politics", "\u521d\u4e2d\u653f\u6cbb", "Social Science"],
    "middle_school_geography": ["Middle School Geography", "\u521d\u4e2d\u5730\u7406", "Social Science"],
    "modern_chinese_history": ["Modern Chinese History", "\u8fd1\u4ee3\u53f2\u7eb2\u8981", "Humanities"],
    "ideological_and_moral_cultivation": ["Ideological and Moral Cultivation", "\u601d\u60f3\u9053\u5fb7\u4fee\u517b\u4e0e\u6cd5\u5f8b\u57fa\u7840", "Humanities"],
    "logic": ["Logic", "\u903b\u8f91\u5b66", "Humanities"],
    "law": ["Law", "\u6cd5\u5b66", "Humanities"],
    "chinese_language_and_literature": ["Chinese Language and Literature", "\u4e2d\u56fd\u8bed\u8a00\u6587\u5b66", "Humanities"],
    "art_studies": ["Art Studies", "\u827a\u672f\u5b66", "Humanities"],
    "professional_tour_guide": ["Professional Tour Guide", "\u5bfc\u6e38\u8d44\u683c", "Humanities"],
    "legal_professional": ["Legal Professional", "\u6cd5\u5f8b\u804c\u4e1a\u8d44\u683c", "Humanities"],
    "high_school_chinese": ["High School Chinese", "\u9ad8\u4e2d\u8bed\u6587", "Humanities"],
    "high_school_history": ["High School History", "\u9ad8\u4e2d\u5386\u53f2", "Humanities"],
    "middle_school_history": ["Middle School History", "\u521d\u4e2d\u5386\u53f2", "Humanities"],
    "civil_servant": ["Civil Servant", "\u516c\u52a1\u5458", "Other"],
    "sports_science": ["Sports Science", "\u4f53\u80b2\u5b66", "Other"],
    "plant_protection": ["Plant Protection", "\u690d\u7269\u4fdd\u62a4", "Other"],
    "basic_medicine": ["Basic Medicine", "\u57fa\u7840\u533b\u5b66", "Other"],
    "clinical_medicine": ["Clinical Medicine", "\u4e34\u5e8a\u533b\u5b66", "Other"],
    "urban_and_rural_planner": ["Urban and Rural Planner", "\u6ce8\u518c\u57ce\u4e61\u89c4\u5212\u5e08", "Other"],
    "accountant": ["Accountant", "\u6ce8\u518c\u4f1a\u8ba1\u5e08", "Other"],
    "fire_engineer": ["Fire Engineer", "\u6ce8\u518c\u6d88\u9632\u5de5\u7a0b\u5e08", "Other"],
    "environmental_impact_assessment_engineer": ["Environmental Impact Assessment Engineer", "\u73af\u5883\u5f71\u54cd\u8bc4\u4ef7\u5de5\u7a0b\u5e08", "Other"],
    "tax_accountant": ["Tax Accountant", "\u7a0e\u52a1\u5e08", "Other"],
    "physician": ["Physician", "\u533b\u5e08\u8d44\u683c", "Other"]
}
hard_list = ['advanced_mathematics', 'discrete_mathematics', 'probability_and_statistics', 'college_physics', 'college_chemistry', 'high_school_mathematics', 'high_school_physics', 'high_school_chemistry']
choices = ["A", "B", "C", "D"]


def main(args):
    model, tokenizer = load_models_tokenizer(args)
    
    dev_result = {}
    for subject_name in tqdm(TASK_NAME_MAPPING.keys()):
        val_file_path = os.path.join(args.eval_data_path, 'val', f'{subject_name}_val.csv')
        dev_file_path = os.path.join(args.eval_data_path, 'dev', f'{subject_name}_dev.csv')
        # test_file_path = os.path.join(args.eval_data_path, 'test', f'{subject_name}_test.csv')
        val_df = pd.read_csv(val_file_path)
        dev_df = pd.read_csv(dev_file_path)
        # test_df = pd.read_csv(test_file_path)

        score = eval_subject(model, tokenizer, subject_name, val_df, dev_df=dev_df, k=5, few_shot=True,
                             save_result_dir=f"outs/ceval_eval_result")
        dev_result[subject_name] = score
    cal_ceval(dev_result)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Test HF checkpoint.')
    parser.add_argument('-c', '--checkpoint-path', type=str, help='Checkpoint path', default="Qwen/Qwen-7B")
    parser.add_argument('-s', '--seed', type=int, default=1234, help='Random seed')

    """Provide extra arguments required for tasks."""
    group = parser.add_argument_group(title='Evaluation options')
    group.add_argument('-d', '--eval_data_path', type=str, required=True,
                       help='Path to eval data')
    group.add_argument("--max-seq-len", type=int, default=2048,
                       help='Size of the output generated text.')
    group.add_argument("--debug", action='store_true', default=False,
                       help='Print infos.')

    args = parser.parse_args()
    set_seed(args.seed)

    main(args)